Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations8649
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory810.8 KiB
Average record size in memory96.0 B

Variable types

Text3
Numeric7
Categorical1

Alerts

benefits is highly overall correlated with interviews and 2 other fieldsHigh correlation
interviews is highly overall correlated with benefits and 2 other fieldsHigh correlation
reviews is highly overall correlated with benefits and 2 other fieldsHigh correlation
salaries is highly overall correlated with benefits and 2 other fieldsHigh correlation
reviews is highly skewed (γ1 = 20.24474875) Skewed
salaries is highly skewed (γ1 = 30.43540064) Skewed
interviews is highly skewed (γ1 = 24.69003962) Skewed
jobs is highly skewed (γ1 = 22.51964104) Skewed
benefits is highly skewed (γ1 = 23.12686003) Skewed
interviews has 229 (2.6%) zeros Zeros
jobs has 3269 (37.8%) zeros Zeros

Reproduction

Analysis started2024-12-28 21:06:43.095780
Analysis finished2024-12-28 21:06:51.137327
Duration8.04 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

name
Text

Distinct8631
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size135.1 KiB
2024-12-28T16:06:51.601544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length63
Median length48
Mean length17.180715
Min length2

Characters and Unicode

Total characters148596
Distinct characters85
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8613 ?
Unique (%)99.6%

Sample

1st rowTCS
2nd rowAccenture
3rd rowCognizant
4th rowWipro
5th rowICICI Bank
ValueCountFrequency (%)
india 464
 
2.3%
services 361
 
1.8%
solutions 332
 
1.6%
technologies 331
 
1.6%
282
 
1.4%
group 244
 
1.2%
industries 218
 
1.1%
engineering 156
 
0.8%
systems 138
 
0.7%
and 133
 
0.7%
Other values (8743) 17572
86.9%
2024-12-28T16:06:52.312368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 12127
 
8.2%
11583
 
7.8%
a 11096
 
7.5%
i 10120
 
6.8%
n 9695
 
6.5%
o 8959
 
6.0%
r 8364
 
5.6%
t 7861
 
5.3%
s 7597
 
5.1%
l 5735
 
3.9%
Other values (75) 55459
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 148596
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 12127
 
8.2%
11583
 
7.8%
a 11096
 
7.5%
i 10120
 
6.8%
n 9695
 
6.5%
o 8959
 
6.0%
r 8364
 
5.6%
t 7861
 
5.3%
s 7597
 
5.1%
l 5735
 
3.9%
Other values (75) 55459
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 148596
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 12127
 
8.2%
11583
 
7.8%
a 11096
 
7.5%
i 10120
 
6.8%
n 9695
 
6.5%
o 8959
 
6.0%
r 8364
 
5.6%
t 7861
 
5.3%
s 7597
 
5.1%
l 5735
 
3.9%
Other values (75) 55459
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 148596
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 12127
 
8.2%
11583
 
7.8%
a 11096
 
7.5%
i 10120
 
6.8%
n 9695
 
6.5%
o 8959
 
6.0%
r 8364
 
5.6%
t 7861
 
5.3%
s 7597
 
5.1%
l 5735
 
3.9%
Other values (75) 55459
37.3%

rating
Real number (ℝ)

Distinct33
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8610013
Minimum1.6
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.1 KiB
2024-12-28T16:06:52.552515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile3.2
Q13.6
median3.9
Q34.1
95-th percentile4.4
Maximum5
Range3.4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.38099445
Coefficient of variation (CV)0.098677628
Kurtosis1.2803192
Mean3.8610013
Median Absolute Deviation (MAD)0.2
Skewness-0.63725594
Sum33393.8
Variance0.14515677
MonotonicityNot monotonic
2024-12-28T16:06:52.799609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
4 992
11.5%
3.9 985
11.4%
4.1 953
11.0%
3.8 921
10.6%
3.7 783
9.1%
4.2 741
8.6%
3.6 538
 
6.2%
3.5 504
 
5.8%
4.3 477
 
5.5%
3.4 342
 
4.0%
Other values (23) 1413
16.3%
ValueCountFrequency (%)
1.6 1
 
< 0.1%
1.9 2
 
< 0.1%
2 3
 
< 0.1%
2.1 3
 
< 0.1%
2.2 5
 
0.1%
2.3 3
 
< 0.1%
2.4 10
 
0.1%
2.5 5
 
0.1%
2.6 19
0.2%
2.7 30
0.3%
ValueCountFrequency (%)
5 1
 
< 0.1%
4.9 10
 
0.1%
4.8 34
 
0.4%
4.7 54
 
0.6%
4.6 93
 
1.1%
4.5 165
 
1.9%
4.4 271
 
3.1%
4.3 477
5.5%
4.2 741
8.6%
4.1 953
11.0%
Distinct83
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size135.1 KiB
2024-12-28T16:06:53.222843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length53
Median length25
Mean length15.326627
Min length3

Characters and Unicode

Total characters132560
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIT Services & Consulting
2nd rowIT Services & Consulting
3rd rowIT Services & Consulting
4th rowIT Services & Consulting
5th rowBanking
ValueCountFrequency (%)
3132
 
16.8%
services 1480
 
7.9%
consulting 1237
 
6.6%
it 1179
 
6.3%
engineering 434
 
2.3%
construction 434
 
2.3%
auto 406
 
2.2%
components 406
 
2.2%
industrial 398
 
2.1%
machinery 361
 
1.9%
Other values (120) 9154
49.2%
2024-12-28T16:06:53.795443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 11842
 
8.9%
e 11446
 
8.6%
i 11027
 
8.3%
9972
 
7.5%
t 8835
 
6.7%
r 7470
 
5.6%
a 6832
 
5.2%
o 6761
 
5.1%
s 6687
 
5.0%
c 5971
 
4.5%
Other values (41) 45717
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 132560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 11842
 
8.9%
e 11446
 
8.6%
i 11027
 
8.3%
9972
 
7.5%
t 8835
 
6.7%
r 7470
 
5.6%
a 6832
 
5.2%
o 6761
 
5.1%
s 6687
 
5.0%
c 5971
 
4.5%
Other values (41) 45717
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 132560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 11842
 
8.9%
e 11446
 
8.6%
i 11027
 
8.3%
9972
 
7.5%
t 8835
 
6.7%
r 7470
 
5.6%
a 6832
 
5.2%
o 6761
 
5.1%
s 6687
 
5.0%
c 5971
 
4.5%
Other values (41) 45717
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 132560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 11842
 
8.9%
e 11446
 
8.6%
i 11027
 
8.3%
9972
 
7.5%
t 8835
 
6.7%
r 7470
 
5.6%
a 6832
 
5.2%
o 6761
 
5.1%
s 6687
 
5.0%
c 5971
 
4.5%
Other values (41) 45717
34.5%

employee_count
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size135.1 KiB
1k-5k
2454 
201-500
1858 
501-1k
1689 
51-200
1365 
5k-10k
446 
Other values (5)
837 

Length

Max length11
Median length8
Mean length6.9566424
Min length5

Characters and Unicode

Total characters60168
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1 Lakh+
2nd row1 Lakh+
3rd row1 Lakh+
4th row1 Lakh+
5th row1 Lakh+

Common Values

ValueCountFrequency (%)
1k-5k 2454
28.4%
201-500 1858
21.5%
501-1k 1689
19.5%
51-200 1365
15.8%
5k-10k 446
 
5.2%
10k-50k 441
 
5.1%
11-50 248
 
2.9%
1-10 69
 
0.8%
1 Lakh+ 50
 
0.6%
50k-1 Lakh 29
 
0.3%

Length

2024-12-28T16:06:53.955183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-28T16:06:54.128420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1k-5k 2454
28.1%
201-500 1858
21.3%
501-1k 1689
19.4%
51-200 1365
15.6%
5k-10k 446
 
5.1%
10k-50k 441
 
5.1%
11-50 248
 
2.8%
lakh 79
 
0.9%
1-10 69
 
0.8%
1 50
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 11667
19.4%
1 10655
17.7%
8728
14.5%
- 8599
14.3%
5 8530
14.2%
k 8479
14.1%
2 3223
 
5.4%
L 79
 
0.1%
a 79
 
0.1%
h 79
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11667
19.4%
1 10655
17.7%
8728
14.5%
- 8599
14.3%
5 8530
14.2%
k 8479
14.1%
2 3223
 
5.4%
L 79
 
0.1%
a 79
 
0.1%
h 79
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11667
19.4%
1 10655
17.7%
8728
14.5%
- 8599
14.3%
5 8530
14.2%
k 8479
14.1%
2 3223
 
5.4%
L 79
 
0.1%
a 79
 
0.1%
h 79
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11667
19.4%
1 10655
17.7%
8728
14.5%
- 8599
14.3%
5 8530
14.2%
k 8479
14.1%
2 3223
 
5.4%
L 79
 
0.1%
a 79
 
0.1%
h 79
 
0.1%

company_age
Real number (ℝ)

Distinct215
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.549659
Minimum1
Maximum545
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.1 KiB
2024-12-28T16:06:54.313219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q116
median27
Q344
95-th percentile103
Maximum545
Range544
Interquartile range (IQR)28

Descriptive statistics

Standard deviation33.666899
Coefficient of variation (CV)0.92112759
Kurtosis17.890277
Mean36.549659
Median Absolute Deviation (MAD)12
Skewness3.1247877
Sum316118
Variance1133.4601
MonotonicityNot monotonic
2024-12-28T16:06:54.495739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 292
 
3.4%
17 262
 
3.0%
23 259
 
3.0%
15 233
 
2.7%
24 222
 
2.6%
27 220
 
2.5%
13 215
 
2.5%
19 212
 
2.5%
11 202
 
2.3%
8 201
 
2.3%
Other values (205) 6331
73.2%
ValueCountFrequency (%)
1 13
 
0.2%
2 14
 
0.2%
3 29
 
0.3%
4 56
 
0.6%
5 60
 
0.7%
6 84
1.0%
7 147
1.7%
8 201
2.3%
9 156
1.8%
10 145
1.7%
ValueCountFrequency (%)
545 1
< 0.1%
358 1
< 0.1%
355 1
< 0.1%
353 1
< 0.1%
333 1
< 0.1%
327 1
< 0.1%
325 2
< 0.1%
296 1
< 0.1%
287 2
< 0.1%
279 1
< 0.1%
Distinct1165
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size135.1 KiB
2024-12-28T16:06:54.822667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length39
Median length26
Mean length8.7677188
Min length3

Characters and Unicode

Total characters75832
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique760 ?
Unique (%)8.8%

Sample

1st rowMumbai
2nd rowDublin
3rd rowTeaneck. New Jersey.
4th rowBangalore/Bengaluru
5th rowMumbai
ValueCountFrequency (%)
mumbai 1331
 
13.8%
new 432
 
4.5%
chennai 425
 
4.4%
noida 393
 
4.1%
delhi 386
 
4.0%
delhi/ncr 335
 
3.5%
pune 320
 
3.3%
bangalore/bengaluru 311
 
3.2%
gurgaon/gurugram 283
 
2.9%
kolkata 264
 
2.7%
Other values (1229) 5168
53.6%
2024-12-28T16:06:55.300215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 10579
 
14.0%
e 5645
 
7.4%
n 5139
 
6.8%
u 5087
 
6.7%
r 4828
 
6.4%
i 4658
 
6.1%
o 3812
 
5.0%
d 3160
 
4.2%
l 3137
 
4.1%
b 2669
 
3.5%
Other values (64) 27118
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 10579
 
14.0%
e 5645
 
7.4%
n 5139
 
6.8%
u 5087
 
6.7%
r 4828
 
6.4%
i 4658
 
6.1%
o 3812
 
5.0%
d 3160
 
4.2%
l 3137
 
4.1%
b 2669
 
3.5%
Other values (64) 27118
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 10579
 
14.0%
e 5645
 
7.4%
n 5139
 
6.8%
u 5087
 
6.7%
r 4828
 
6.4%
i 4658
 
6.1%
o 3812
 
5.0%
d 3160
 
4.2%
l 3137
 
4.1%
b 2669
 
3.5%
Other values (64) 27118
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 10579
 
14.0%
e 5645
 
7.4%
n 5139
 
6.8%
u 5087
 
6.7%
r 4828
 
6.4%
i 4658
 
6.1%
o 3812
 
5.0%
d 3160
 
4.2%
l 3137
 
4.1%
b 2669
 
3.5%
Other values (64) 27118
35.8%

reviews
Real number (ℝ)

High correlation  Skewed 

Distinct869
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411.81836
Minimum66
Maximum66700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.1 KiB
2024-12-28T16:06:55.458480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile70
Q193
median148
Q3295
95-th percentile1300
Maximum66700
Range66634
Interquartile range (IQR)202

Descriptive statistics

Standard deviation1572.5531
Coefficient of variation (CV)3.81856
Kurtosis585.43433
Mean411.81836
Median Absolute Deviation (MAD)68
Skewness20.244749
Sum3561817
Variance2472923.3
MonotonicityDecreasing
2024-12-28T16:06:55.643456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69 120
 
1.4%
72 104
 
1.2%
68 102
 
1.2%
67 99
 
1.1%
76 97
 
1.1%
71 94
 
1.1%
79 93
 
1.1%
75 92
 
1.1%
73 89
 
1.0%
80 86
 
1.0%
Other values (859) 7673
88.7%
ValueCountFrequency (%)
66 67
0.8%
67 99
1.1%
68 102
1.2%
69 120
1.4%
70 82
0.9%
71 94
1.1%
72 104
1.2%
73 89
1.0%
74 78
0.9%
75 92
1.1%
ValueCountFrequency (%)
66700 1
< 0.1%
42500 1
< 0.1%
38400 1
< 0.1%
35400 1
< 0.1%
30900 1
< 0.1%
30600 1
< 0.1%
29100 1
< 0.1%
27000 1
< 0.1%
25300 1
< 0.1%
24800 1
< 0.1%

salaries
Real number (ℝ)

High correlation  Skewed 

Distinct1172
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2429.1582
Minimum4
Maximum734800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.1 KiB
2024-12-28T16:06:55.995436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile264.4
Q1498
median808
Q31600
95-th percentile6800
Maximum734800
Range734796
Interquartile range (IQR)1102

Descriptive statistics

Standard deviation14849.975
Coefficient of variation (CV)6.1132187
Kurtosis1152.7582
Mean2429.1582
Median Absolute Deviation (MAD)392
Skewness30.435401
Sum21009789
Variance2.2052176 × 108
MonotonicityNot monotonic
2024-12-28T16:06:56.166379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100 296
 
3.4%
1200 252
 
2.9%
1300 226
 
2.6%
1400 187
 
2.2%
1000 173
 
2.0%
1500 156
 
1.8%
1700 152
 
1.8%
1600 136
 
1.6%
1800 91
 
1.1%
1900 89
 
1.0%
Other values (1162) 6891
79.7%
ValueCountFrequency (%)
4 2
< 0.1%
5 2
< 0.1%
7 1
< 0.1%
8 2
< 0.1%
9 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 2
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
ValueCountFrequency (%)
734800 1
< 0.1%
513400 1
< 0.1%
496800 1
< 0.1%
413100 1
< 0.1%
370300 1
< 0.1%
336600 1
< 0.1%
251900 1
< 0.1%
236300 1
< 0.1%
197400 1
< 0.1%
176600 1
< 0.1%

interviews
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct278
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.523876
Minimum0
Maximum5600
Zeros229
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size135.1 KiB
2024-12-28T16:06:56.344025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q316
95-th percentile74
Maximum5600
Range5600
Interquartile range (IQR)12

Descriptive statistics

Standard deviation131.62425
Coefficient of variation (CV)5.3671882
Kurtosis776.18698
Mean24.523876
Median Absolute Deviation (MAD)5
Skewness24.69004
Sum212107
Variance17324.944
MonotonicityNot monotonic
2024-12-28T16:06:56.509723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 712
 
8.2%
4 684
 
7.9%
5 640
 
7.4%
2 620
 
7.2%
6 534
 
6.2%
7 479
 
5.5%
1 461
 
5.3%
8 397
 
4.6%
9 338
 
3.9%
10 285
 
3.3%
Other values (268) 3499
40.5%
ValueCountFrequency (%)
0 229
 
2.6%
1 461
5.3%
2 620
7.2%
3 712
8.2%
4 684
7.9%
5 640
7.4%
6 534
6.2%
7 479
5.5%
8 397
4.6%
9 338
3.9%
ValueCountFrequency (%)
5600 1
< 0.1%
4500 1
< 0.1%
3800 1
< 0.1%
3300 2
< 0.1%
3200 1
< 0.1%
2300 1
< 0.1%
2200 2
< 0.1%
1800 1
< 0.1%
1700 2
< 0.1%
1500 1
< 0.1%

jobs
Real number (ℝ)

Skewed  Zeros 

Distinct293
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.341774
Minimum0
Maximum4100
Zeros3269
Zeros (%)37.8%
Negative0
Negative (%)0.0%
Memory size135.1 KiB
2024-12-28T16:06:56.670975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q314
95-th percentile82
Maximum4100
Range4100
Interquartile range (IQR)14

Descriptive statistics

Standard deviation93.405106
Coefficient of variation (CV)4.5917877
Kurtosis758.00695
Mean20.341774
Median Absolute Deviation (MAD)2
Skewness22.519641
Sum175936
Variance8724.5138
MonotonicityNot monotonic
2024-12-28T16:06:56.813494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3269
37.8%
1 641
 
7.4%
2 476
 
5.5%
3 363
 
4.2%
4 283
 
3.3%
5 247
 
2.9%
6 220
 
2.5%
8 182
 
2.1%
7 176
 
2.0%
9 149
 
1.7%
Other values (283) 2643
30.6%
ValueCountFrequency (%)
0 3269
37.8%
1 641
 
7.4%
2 476
 
5.5%
3 363
 
4.2%
4 283
 
3.3%
5 247
 
2.9%
6 220
 
2.5%
7 176
 
2.0%
8 182
 
2.1%
9 149
 
1.7%
ValueCountFrequency (%)
4100 1
< 0.1%
3500 1
< 0.1%
2300 1
< 0.1%
2000 1
< 0.1%
1800 1
< 0.1%
1700 1
< 0.1%
1400 1
< 0.1%
1300 1
< 0.1%
1200 1
< 0.1%
1100 2
< 0.1%

benefits
Real number (ℝ)

High correlation  Skewed 

Distinct473
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.739276
Minimum0
Maximum11300
Zeros15
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size135.1 KiB
2024-12-28T16:06:56.959545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q112
median20
Q343
95-th percentile194.6
Maximum11300
Range11300
Interquartile range (IQR)31

Descriptive statistics

Standard deviation238.36675
Coefficient of variation (CV)4.0580472
Kurtosis790.84249
Mean58.739276
Median Absolute Deviation (MAD)11
Skewness23.12686
Sum508036
Variance56818.709
MonotonicityNot monotonic
2024-12-28T16:06:57.122809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 332
 
3.8%
13 329
 
3.8%
9 328
 
3.8%
12 320
 
3.7%
10 298
 
3.4%
14 291
 
3.4%
15 287
 
3.3%
8 277
 
3.2%
17 245
 
2.8%
7 231
 
2.7%
Other values (463) 5711
66.0%
ValueCountFrequency (%)
0 15
 
0.2%
1 16
 
0.2%
2 31
 
0.4%
3 65
 
0.8%
4 101
 
1.2%
5 164
1.9%
6 212
2.5%
7 231
2.7%
8 277
3.2%
9 328
3.8%
ValueCountFrequency (%)
11300 1
< 0.1%
7000 1
< 0.1%
5700 1
< 0.1%
5000 1
< 0.1%
4900 1
< 0.1%
4100 1
< 0.1%
4000 1
< 0.1%
3900 1
< 0.1%
3700 1
< 0.1%
3600 1
< 0.1%

Interactions

2024-12-28T16:06:49.777285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:43.566959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:44.585215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:45.775527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:46.725866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:47.684380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:48.582202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:49.923414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:43.700307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:44.725174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:45.934770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:46.866893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:47.834590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:48.708382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:50.059803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:43.833291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:45.081919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:46.079990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:46.993443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:47.956289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:48.853962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:50.195093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:43.996967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:45.244755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:46.217686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:47.143928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:48.101594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:49.142841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:50.322290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:44.136596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:45.397228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:46.351471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:47.285512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:48.239335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:49.292032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:50.450871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:44.288460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:45.533205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:46.475167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:47.417130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:48.363306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:49.448712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:50.602078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:44.430680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:45.651401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:46.595252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:47.549115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:48.468915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-28T16:06:49.602984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-28T16:06:57.235209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
benefitscompany_ageemployee_countinterviewsjobsratingreviewssalaries
benefits1.0000.1120.2180.6930.2840.1830.8600.739
company_age0.1121.0000.0670.032-0.1850.1360.1250.135
employee_count0.2180.0671.0000.2120.1180.0800.2110.210
interviews0.6930.0320.2121.0000.428-0.0350.7720.789
jobs0.284-0.1850.1180.4281.000-0.1350.3010.392
rating0.1830.1360.080-0.035-0.1351.0000.089-0.052
reviews0.8600.1250.2110.7720.3010.0891.0000.845
salaries0.7390.1350.2100.7890.392-0.0520.8451.000

Missing values

2024-12-28T16:06:50.763381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-28T16:06:51.012411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

nameratingcompany_typeemployee_countcompany_agehead_quartersreviewssalariesinterviewsjobsbenefits
0TCS3.8IT Services & Consulting1 Lakh+55Mumbai66700734800560021311300
1Accenture4.1IT Services & Consulting1 Lakh+34Dublin42500513400380041007000
2Cognizant3.9IT Services & Consulting1 Lakh+29Teaneck. New Jersey.3840049680033004975700
3Wipro3.8IT Services & Consulting1 Lakh+78Bangalore/Bengaluru3540037030033003164900
4ICICI Bank4.0Banking1 Lakh+29Mumbai3090013610017002143700
5HDFC Bank3.9Banking1 Lakh+29Mumbai3060012360014003763200
6Infosys3.9IT Services & Consulting1 Lakh+42Bengaluru/Bangalore2910041310045007795000
7Capgemini3.8IT Services & Consulting1 Lakh+56Paris2700033660023005123900
8Tech Mahindra3.7IT Services & Consulting1 Lakh+37Pune25300236300220011003500
9HCLTech3.7IT Services & Consulting1 Lakh+32Noida2480025190022005744000
nameratingcompany_typeemployee_countcompany_agehead_quartersreviewssalariesinterviewsjobsbenefits
9984Flowserve Sanmar3.5Chemicals51-20036Chennai66434507
9987Syniti4.4IT Services & Consulting201-50027Needham6675110514
9988Tech Firefly3.2IT Services & Consulting1k-5k7Santa Clara66281345
9991TSMT Technology India2.8Semiconductors201-50026Taoyuan66519278
9992Contizant Technologies4.1IT Services & Consulting11-505Gurgaon/Gurugram6638513017
9994Ecolog International4.6Logistics10k-50k20Düsseldorf66620014
9995Advocate4.3IT Services & Consulting201-50022Atlanta66605409
9996Adamas University3.0Education & Training51-2009Kolkata66342607
9997Nagarjuna Cements4.0Engineering & Construction501-1k28Hyderabad66381207
9999Success Pact Consulting3.1Recruitment51-20012Noida66223011713